Probabilistic Safety Assessment using Quantitative Analysis Techniques : Application in the Heavy Automotive Industry

Safety is considered as one of the most important areas in future research and development within the automotive industry. New functionality, such as driver support and active/passive safety systems are examples where development mainly focuses on safety. At the same time, the trend is towards more complex systems, increased software dependence and an increasing amount of sensors and actuators, resulting in a higher risk associated with software and hardware failures. In the area of functional safety, standards such as ISO 26262 assess safety mainly focusing on qualitative assessment techniques, whereas usage of quantitative techniques is a growing area in academic research. This thesis considers the field functional safety, with the emphasis on how hardware and software failure probabilities can be used to quantitatively assess safety of a system/function. More specifically, this thesis presents a method for quantitative safety assessment using Bayesian networks for probabilistic modeling. Since the safety standard ISO 26262 is becoming common in the automotive industry, the developed method is adjusted to use information gathered when implementing this standard. Continuing the discussion about safety, a method for modeling faults and failures using Markov models is presented. These models connect to the previous developed Bayesian network and complete the quantitative safety assessment. Furthermore, the potential for implementing the discussed models in the Modelica language is investigated, aiming to find out if models such as these could be useful in practice to simplify design work, in order to meet future safety goals